Inverse modelling via differential search algorithm for interpreting magnetic anomalies caused by 2D dyke-shaped bodies

Balkaya, Çağlayan; Kaftan, Ilknur

doi:10.1007/s12040-021-01614-1

Cited by 23 publications

(11 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since geophysical inverse problems are ill‐posed and there are numerous parameter sets in the model space that can yield the same misfit value (within a certain error range) there may be uncertainties in the solutions obtained (Sen & Stoffa, 2013; Tarantola, 2005). Therefore, some analyses should be performed before and after the inversion process to determine the reliability of the results (A. Balkaya & Kaftan, 2021; Ekinci, Balkaya, & Göktürkler, 2021; Ekinci, Balkaya, Göktürkler, & Özyalın, 2021; Pallero et al., 2017). As a pre‐inversion analysis, objective function topography maps are produced to understand the dependencies between the model parameters (Ai et al., 2022; Ç. Balkaya et al., 2017; Ekinci et al., 2016; Fernández‐Martínez et al., 2010; Pace et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

Self Cite

View full text Add to dashboard Cite

Geomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems.

show abstract

Section: Methodsmentioning

confidence: 99%

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…A nonstructured model discretization mesh was performed through Delaunay triangular cells to accurately represent the rugged observation surface and recover complex-shaped sources of the Shavaz iron-oxide ore deposit in Iran (Abedi, 2020). Moreover, an inverse modeling analysis on the interpretation of magnetic anomalies induced by 2D dykeshaped bodies was accomplished by employing the differential search algorithm (DSA), a novel metaheuristic roused by the migration of super-organisms (Balkaya & Kaftan, 2021;Chen et al, 2021). A new imaging method has been developed based on calculating the correlation coefficient of the analytic signals of the measured magnetic anomaly and the calculated response of some geometrically simple interpretive models of sheets, cylinders, and spheres.…”

Section: Introductionmentioning

confidence: 99%

Geobody Estimation by Bhattacharyya Method Utilizing Nonlinear Inverse Modeling of Magnetic Data in Baba-Ali Iron Deposit, NW Iran

Kianoush

Khah²,

Hosseini

et al. 2023

Preprint

View full text Add to dashboard Cite

Geophysical investigations are one of the leading efforts in any exploration project that must be performed ahead of time due to the high excavation expenditures, heavy machinery, and cost reduction. One of the essential geophysical concerns is the estimation of the physical and geometrical parameters of the reserve (geobody), which is done by exploiting the nonlinear inverse modeling of magnetic data. These parameters include depth, volume, shape factor, and type of storage. The present investigation aims to prepare and model magnetic data for proposed drilling sites in the Baba Ali Iron ore deposit in the Hamedan province of Iran. The areal and depth extent of the iron ore geobody was unknown. The Bhattacharyya method by MATLAB software coding was used to minimize the target function and potential field data and create the best fit for the magnetic data. The residual anomaly and model graphs exhibited an excellent two-dimensional conformation in this modeling. Also, 3D modeling correctly reconstructs the properties of the productive resources of anomalies. After preparing full magnetic maps, the magnetic lenses distinguished in four anomalies of surface depths, 20, 50, and deeper than 50 meters for this zone. This magnetite lens for the first zone was estimated based on filters applied on the entire magnetic map so that the lens's depth is trivial and almost zero. Their specific gravity is calculated as 4.77 t/m3, and their initial storage capacity is suggested to be about 95,400 tones at most. Finally, to complete the preliminary explorations of the specified area, exploratory drilling is suggested for three points by inverse modeling.

show abstract

Section: Introductionmentioning

confidence: 99%

Geobody Estimation by Bhattacharyya Method Utilizing Nonlinear Inverse Modeling of Magnetic Data in Baba-Ali Iron Deposit, NW Iran

Kianoush

Khah²,

Hosseini

et al. 2023

Preprint

View full text Add to dashboard Cite

Geophysical investigations are one of the leading efforts in any exploration project that must be performed ahead of time due to the high excavation expenditures, heavy machinery, and cost reduction. One of the essential geophysical concerns is the estimation of the physical and geometrical parameters of the reserve (geobody), which is done by exploiting the nonlinear inverse modeling of magnetic data. These parameters include depth, volume, shape factor, and type of storage. The present investigation aims to prepare and model magnetic data for proposed drilling sites in the Baba Ali Iron ore deposit in the Hamedan province of Iran. The area is covered with 1000 points of geomagnetic reading with an almost 5*10 m regularly spaces grid trending WE. The areal and depth extent of the iron ore geobody was unknown. The Bhattacharyya method by MATLAB software coding was used to minimize the target function and potential field data and create the best fit for the measured and modelled magnetic data. The residual anomaly and model graphs exhibited an excellent two-dimensional conformation in this modeling. Also, 3D modeling correctly reconstructs the properties of the productive resources of anomalies. After preparing full magnetic maps, the magnetic lenses distinguished in four anomalies of surface depths, 20, 50, and deeper than 50 meters for this zone. This magnetite lens for the first zone was estimated based on analytical signal filters applied on the entire magnetic map so that the lens's depth is trivial and almost zero. Due to specific gravity calculated as 4.77 t/m3, initial storage capacity is suggested to be about 95,400 tons of magnetite, pyrite, and hematite minerals at most in an area about 6 Km2. Finally, to complete the preliminary explorations of the specified area, exploratory drilling is suggested for three points by inverse modeling.

show abstract

Inverse modelling via differential search algorithm for interpreting magnetic anomalies caused by 2D dyke-shaped bodies

Cited by 23 publications

References 78 publications

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Geobody Estimation by Bhattacharyya Method Utilizing Nonlinear Inverse Modeling of Magnetic Data in Baba-Ali Iron Deposit, NW Iran

Geobody Estimation by Bhattacharyya Method Utilizing Nonlinear Inverse Modeling of Magnetic Data in Baba-Ali Iron Deposit, NW Iran

Contact Info

Product

Resources

About